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Structural basis of receptor recognition by SARS-CoV-2
Nature, 2020 A novel severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) recently emerged and is rapidly spreading in humans, causing COVID-19 1 , 2 . A key to tackling this pandemic is to understand the receptor recognition mechanism of the virus,
Jian Shang, Gang Ye, Ke Shi
exaly +2 more sources
Trainable joint bilateral filters for enhanced prediction stability in low-dose CT
Scientific Reports, 2022 Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality.
Fabian Wagner +9 more
doaj +1 more source
Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN
Journal of Mathematics, 2021 Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and ...
Mengmeng Huang, Fang Liu, Xianfa Meng
doaj +1 more source
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition [PDF]
AAAI Conference on Artificial Intelligence, 2018 Dynamics of human body skeletons convey significant information for human action recognition. Conventional approaches for modeling skeletons usually rely on hand-crafted parts or traversal rules, thus resulting in limited expressive power and ...
Sijie Yan, Yuanjun Xiong, Dahua Lin
semanticscholar +1 more source
Scene text removal via cascaded text stroke detection and erasing
Computational Visual Media, 2021 Recent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results.
Xuewei Bian +5 more
doaj +1 more source
Deep Residual Learning for Image Recognition [PDF]
Computer Vision and Pattern Recognition, 2015 Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
Kaiming He +3 more
semanticscholar +1 more source
FaceNet: A unified embedding for face recognition and clustering [PDF]
Computer Vision and Pattern Recognition, 2015 Despite significant recent advances in the field of face recognition [10, 14, 15, 17], implementing face verification and recognition efficiently at scale presents serious challenges to current approaches.
Florian Schroff +2 more
semanticscholar +1 more source
Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images
Scientific Reports, 2021 In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear
Xia Zhong +8 more
doaj +1 more source
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset [PDF]
Computer Vision and Pattern Recognition, 2017 The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks. This paper re-evaluates state-
João Carreira, Andrew Zisserman
semanticscholar +1 more source
Learning Transferable Architectures for Scalable Image Recognition [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017 Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest.
Barret Zoph +3 more
semanticscholar +1 more source